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- W4285251638 abstract "Diabetes mellitus is a concern all over the world. Early prediction is necessary to prevent complications associated with it. The author evaluated performance of six classifiers, namely support vector machine (SVM), random forest (RF), decision tree (DT), logistic regression (LR), gradient boosting classifier (GBC) and K-nearest neighbor (KNN) for early prediction of type 2 diabetes. The accuracy of models was calculated using feature selection. Optimization was done using grid search method. Performance measures precision, recall and F1-score and area under the receiver operating characteristics (AUROC) curve were calculated. After applying several feature selection methods, SVM provided an accuracy of 82.14%. After applying the grid search technique, accuracies of GBC and SVM had 86.67% accuracy. Precision and recall values of SVM and GBC were highest, i.e., 0.87. Overall, GBC and SVM had best precision, recall and F-1 scores. According to ROC curve analysis, best performance was observed with SVM and RF." @default.
- W4285251638 created "2022-07-14" @default.
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- W4285251638 date "2022-01-01" @default.
- W4285251638 modified "2023-09-25" @default.
- W4285251638 title "Performance Evaluation of Machine Learning Classifiers for Prediction of Type 2 Diabetes Using Stress-Related Parameters" @default.
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- W4285251638 doi "https://doi.org/10.1007/978-981-19-2211-4_8" @default.
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